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Record W3082149586 · doi:10.14802/jmd.20052

Update on Current Technologies for Deep Brain Stimulation in Parkinson’s Disease

2020· article· en· W3082149586 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Movement Disorders · 2020
Typearticle
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsOntario Brain InstituteToronto Western HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsDeep brain stimulationMedicineFlexibility (engineering)Parkinson's diseaseTherapeutic windowNeuroscienceMovement disordersStimulationNeuromodulationLead (geology)DystoniaDiseasePhysical medicine and rehabilitationPsychiatryPsychology

Abstract

fetched live from OpenAlex

Deep brain stimulation (DBS) is becoming increasingly central in the treatment of patients with Parkinson's disease and other movement disorders. Recent developments in DBS lead and implantable pulse generator design provide increased flexibility for programming, potentially improving the therapeutic benefit of stimulation. Directional DBS leads may increase the therapeutic window of stimulation by providing a means of avoiding current spread to structures that might give rise to stimulation-related side effects. Similarly, control of current to individual contacts on a DBS lead allows for shaping of the electric field produced between multiple active contacts. The following review aims to describe the recent developments in DBS system technology and the features of each commercially available DBS system. The advantages of each system are reviewed, and general considerations for choosing the most appropriate system are discussed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.297
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it